{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 预测2012年每天的单车共享数量\n",
    "\n",
    "请在Capital Bikeshare （美国Washington, D.C.的一个共享单车公司）提供的自行车数据上进行回归分析。训练数据为2011年的数据，要求预测2012年每天的单车共享数量。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 1、导入必要的工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np  # 矩阵操作\n",
    "import pandas as pd # SQL数据处理\n",
    "\n",
    "from sklearn.metrics import r2_score  #评价回归预测模型的性能\n",
    "\n",
    "import matplotlib.pyplot as plt   #画图\n",
    "import seaborn as sns\n",
    "\n",
    "# 图形出现在Notebook里而不是新窗口\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2、数据探索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# path to where the data lies\n",
    "#dpath = './data/'\n",
    "data = pd.read_csv(\"day.csv\")\n",
    "\n",
    "#通过观察前5行，了解数据每列（特征）的概况\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  2.2 数据基本信息\n",
    "样本数目、特征维数\n",
    "每个特征的类型、空值样本的数目、数据类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(731, 16)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   731.000000   731.000000   731.000000  \n",
       "mean    848.176471  3656.172367  4504.348837  \n",
       "std     686.622488  1560.256377  1937.211452  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     315.500000  2497.000000  3152.000000  \n",
       "50%     713.000000  3662.000000  4548.000000  \n",
       "75%    1096.000000  4776.500000  5956.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 单变量分布分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6q0LKkpnZNHd08/7eY3aHEjI0gajP+dPacpJjI5kyJsnuUJQaMgsnppEWH8Ur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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111a54fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 目标y（房屋价格）的直方图／分布\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.cnt.values, bins=30, kde=True)\n",
    "plt.xlabel('Median value of cnt', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Su02MwBkkajbNuOq9AlC6vZm0IczDW+ouKQ2QzSoNmHV0hv+hPUPa50FlUB+/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\nIgAEQRDaFBEAgiAIbcr/Bw1EbIWHHgM8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111a88f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 单个特征散点图\n",
    "plt.scatter(range(data.shape[0]), data[\"cnt\"].values,color='purple')\n",
    "plt.title(\"Distribution of cnt\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输入属性的直方图/分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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H+6mY2UXb/UaSr/fr7kxyXpLzgSuAn0/y7SS39tu+vr/owsNJ7k3yxkXPc26S/Uneku4i\nJweSvL5ftw14LfDW/vn+Zn3/NqTOSBeJlqbEy4GfBV4P/AHwp8DZ/TlDLgV+sqru608nfERV3ZPk\nD/neKZdDwMuAe+kuZvKZJF+uqlv69T9Md7Ktk+jOMf/RJJ+squ1JfgqnXDRhvkNXC75YVTuq6lHg\nr4Dn9uOPAkcDpyU5sqr2VdU9yz1JVf1tVd1TnX8APgf89KJNvgP8XlV9p6p2AN8GTl3quaRJsNDV\ngvsXLf838KQkm6rqbuAy4ErgUJIPJ/mR5Z4kyQVJ/jnJA0kepDup2eZFm3yzHr+oxcK+jhnbn0Ia\nkYWuplXVtVX1ArprQhbwjoVVi7dLcjTdOeTfSXdlmuOAHSx98YIldzWexNLwLHQ1K93V21/Ul/X/\nAv9DNw0DcBDYmmThe+AouumZeeCRJBfQnVN8UAfpTmErTYyFrpYdDbyd7kLN99NdA/KKft3CZcW+\nmeSWqnoY+DXgeuA/gdewuoupXEU3V/9gkk+OI7y0Wn6wSJIa4Tt0SWqEhS5JjbDQJakRFrokNcJC\nl6RGWOiS1AgLXZIaYaFLUiMsdElqxP8D/6o2PoF9RJYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1ad4d1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.instant.values, bins=30, kde=False)\n",
    "plt.xlabel('instant', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+S+q87GYcsETOSzdFcAOwFbiiqmY9J1W1A7gbeMTerXI4Q4wF4EXdtOAnkxw2YP1i8NfA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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a271470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.season.values, bins=30, kde=False)\n",
    "plt.xlabel('season', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jJPnzxq6i1QuEQZekRhh0NSPJo0n+pPshAs8kuSHJ6iSfT/J0kn9KcmqSySSVZDrJviRP\ndB+a9Owl2tcBv5vk+0kenPMSL0/yr93vdXeS08eyo9ICDLpa89v0Lhv/ReC36H1M6XXA6fS+3t89\nZ9tfBc6h99kof5bk3Kq6i95HBtxSVSdV1Xlztn8L8HZ6H+t6PPDHI94XaVEMulrzN1V1sPsM8X8G\ndlbV/VX1I+B2ep9x/6wPVNUPq+pB4EF6P+Xo+Xyiqr5ZVT8EbqX3eTHSMcOgqzUH59z/4TzLJ81Z\nfnzO/R8c8dh8Fru9dFQZdOm5/MQ6rUgGXXqug8BkEv98aEXxC1Z6rn/obr+T5L6xTiItgp+HLkmN\n8Ahdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhrxfzsdnvoacgwyAAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a1e4898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.mnth.values, bins=30, kde=False)\n",
    "plt.xlabel('mnth', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CjvnU4JhPDVMfc6qe8z6pJKkjLoEgSZ1bNUE/blmFJC9J8ol2/L4kG5e/yskaMOb3J/l8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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a2130b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.holiday.values, bins=30, kde=False)\n",
    "plt.xlabel('holiday', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x112fc6dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.weekday.values, bins=30, kde=False)\n",
    "plt.xlabel('weekday', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x112fa4470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.workingday.values, bins=30, kde=False)\n",
    "plt.xlabel('workingday', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x112f230b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.weathersit.values, bins=30, kde=False)\n",
    "plt.xlabel('weathersit', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3urJ8W3fbOxmMigF+hcH/Kf8T+C5w85Tk+jxwH3BD93XllOT6S+DmLtP2Ryra5cy137HL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\nJalRFrwkNcqCl6RGWfCS1Kj/B9Dvjy+VIA0EAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111b2b400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.temp.values, bins=30, kde=False)\n",
    "plt.xlabel('temp', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8kW5q1Dev+Ld+kG6ypeX17wXOoptg69nAm5M8YcX2pwAfBR4LfAj4+yQPncLXKA1ioWtT\nq6qPVtVtVXVfVV0M3Eh3o4IHSBK6mzv8TlV9varuAf6Ybla/Zd8B/qiqvkM30+RhwLuq6p6q2kU3\nR8tTV2x/bVVd0m//Dro/BidO4cuUBjl4owNI40jya8Br6W49CPBIuiLeex7tBeARwLVdt3efTnfb\nsWVfq/vn3/5W//6OFR//Vv/vL/vezRCq6r7+pgc/tK4vRJoAC12bVn9Xm/cCz6M7vPLdJDvpinrv\nWefupCvkp1TVf04owvfmzu5vcjBv8/PrAOMhF21my/c8XQJI8ut0N8CAbmS9Jckh0I2g6cr/giSH\n99sfmeRnx3j+ZyR5cZKDgdcA36a7T6y0ISx0bVpVdT3w58BVdAX+43RzVUN3L8pdwH8lubNf9wbg\nJuDqJN+gm5v+yWNEuBT4VeAu4KXAi/vj6dKGcD50aR2SnA88sarO2ugs0jJH6JLUCAtdkhrhIRdJ\naoQjdElqhIUuSY2w0CWpERa6JDXCQpekRljoktSI/wduZ1UY6P090gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a261278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.atemp.values, bins=30, kde=False)\n",
    "plt.xlabel('atemp', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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a4e8ZhHkL86iPO2bfq+rRqjqlqmarapbB+wevrqr5lSl3rPr8vP8rgzfESXIKgymY+6da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\nA12SGvH/Kms95FMNP+8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11301dd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.hum.values, bins=30, kde=False)\n",
    "plt.xlabel('hum', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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L/pbBhwnwEOA/gOuBS4AnLPQv1xNq97MZfLv/ELgDuHqha55g278E3A5c0f2tXeiaJ9j2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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1ac979b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.windspeed.values, bins=30, kde=False)\n",
    "plt.xlabel('windspeed', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 两两特征之间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cols = data.columns\n",
    "data_corr= data.corr().abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15, 15)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_corr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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shBBCCCGEEMIy6bEQQgghhBAiK+nl71gIIYQQQgghhEXSsMhkro0q473vJ1rt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kGoCqiwbdqQ7waPwdW+P/aOHWjZ2vA7j0t3e3Zuy958U8mbJa8aFsFAIyFEMeAJcBxNA6MV8AhN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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1ad21c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(14,9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "sns.heatmap(data_corr,mask=data_corr<1,cbar=False)\n",
    "plt.savefig('bike_coor.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 数据准备"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "训练数据和测试数据分割(请将 2012 年的数据作为测试数据);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train = data[data.yr==0]\n",
    "data_test = data[data.yr==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>366</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.375621</td>\n",
       "      <td>0.692500</td>\n",
       "      <td>0.192167</td>\n",
       "      <td>686</td>\n",
       "      <td>1608</td>\n",
       "      <td>2294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>367</td>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.273043</td>\n",
       "      <td>0.252304</td>\n",
       "      <td>0.381304</td>\n",
       "      <td>0.329665</td>\n",
       "      <td>244</td>\n",
       "      <td>1707</td>\n",
       "      <td>1951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>368</td>\n",
       "      <td>2012-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.126275</td>\n",
       "      <td>0.441250</td>\n",
       "      <td>0.365671</td>\n",
       "      <td>89</td>\n",
       "      <td>2147</td>\n",
       "      <td>2236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>369</td>\n",
       "      <td>2012-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.107500</td>\n",
       "      <td>0.119337</td>\n",
       "      <td>0.414583</td>\n",
       "      <td>0.184700</td>\n",
       "      <td>95</td>\n",
       "      <td>2273</td>\n",
       "      <td>2368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>370</td>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.265833</td>\n",
       "      <td>0.278412</td>\n",
       "      <td>0.524167</td>\n",
       "      <td>0.129987</td>\n",
       "      <td>140</td>\n",
       "      <td>3132</td>\n",
       "      <td>3272</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "365      366  2012-01-01       1   1     1        0        0           0   \n",
       "366      367  2012-01-02       1   1     1        1        1           0   \n",
       "367      368  2012-01-03       1   1     1        0        2           1   \n",
       "368      369  2012-01-04       1   1     1        0        3           1   \n",
       "369      370  2012-01-05       1   1     1        0        4           1   \n",
       "\n",
       "     weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "365           1  0.370000  0.375621  0.692500   0.192167     686        1608   \n",
       "366           1  0.273043  0.252304  0.381304   0.329665     244        1707   \n",
       "367           1  0.150000  0.126275  0.441250   0.365671      89        2147   \n",
       "368           2  0.107500  0.119337  0.414583   0.184700      95        2273   \n",
       "369           1  0.265833  0.278412  0.524167   0.129987     140        3132   \n",
       "\n",
       "      cnt  \n",
       "365  2294  \n",
       "366  1951  \n",
       "367  2236  \n",
       "368  2368  \n",
       "369  3272  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(365, 7)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train = data_train['cnt'].values\n",
    "X_train = data_train.drop(['yr','cnt','dteday','casual','registered','weekday','holiday','mnth','season'], axis = 1)\n",
    "y_test=data_test['cnt'].values\n",
    "X_test=data_test.drop(['yr','cnt','dteday','casual','registered','weekday','holiday','mnth','season'], axis = 1)\n",
    "#用于后续显示权重系数对应的特征\n",
    "X=data.drop(['yr','cnt','dteday','casual','registered','weekday','holiday','mnth','season'], axis = 1)\n",
    "columns = X.columns\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5 数据预处理／特征工程\n",
    "\n",
    "特征工程是实际任务中特别重要的环节。\n",
    "\n",
    "scikit learn中提供的数据预处理功能：\n",
    "http://scikit-learn.org/stable/modules/preprocessing.html\n",
    "http://scikit-learn.org/stable/modules/classes.html#module- sklearn.feature_extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#发现各特征差异较大，需要进行数据标准化预处理\n",
    "#标准化的目的在于避免原始特征值差异过大，导致训练得到的参数权重不归一，无法比较各特征的重要性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wangchunming/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 分别初始化对特征和目标值的标准化器\n",
    "ss_X = StandardScaler()\n",
    "ss_y = StandardScaler()\n",
    "\n",
    "# 分别对训练和测试数据的特征以及目标值进行标准化处理\n",
    "X_train = ss_X.fit_transform(X_train)\n",
    "X_test = ss_X.transform(X_test)\n",
    "\n",
    "#对y做标准化不是必须\n",
    "#对y标准化的好处是不同问题的w差异不太大，同时正则参数的范围也有限\n",
    "y_train = ss_y.fit_transform(y_train.reshape(-1, 1))\n",
    "y_test = ss_y.transform(y_test.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 3、确定模型类型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 尝试缺省参数的线性回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[0.685898043475]</td>\n",
       "      <td>temp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[0.227282363093]</td>\n",
       "      <td>instant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[0.0164206873069]</td>\n",
       "      <td>workingday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[-0.00701909533463]</td>\n",
       "      <td>atemp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[-0.0608277934045]</td>\n",
       "      <td>hum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[-0.136472395537]</td>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[-0.209508309028]</td>\n",
       "      <td>weathersit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  coef     columns\n",
       "3     [0.685898043475]        temp\n",
       "0     [0.227282363093]     instant\n",
       "1    [0.0164206873069]  workingday\n",
       "4  [-0.00701909533463]       atemp\n",
       "5   [-0.0608277934045]         hum\n",
       "6    [-0.136472395537]   windspeed\n",
       "2    [-0.209508309028]  weathersit"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 线性回归\n",
    "#class sklearn.linear_model.LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=1)\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# 使用默认配置初始化\n",
    "lr = LinearRegression()\n",
    "\n",
    "# 训练模型参数\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "# 预测\n",
    "y_test_pred_lr = lr.predict(X_test)\n",
    "y_train_pred_lr = lr.predict(X_train)\n",
    "\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef\":list((lr.coef_.T))})\n",
    "fs.sort_values(by=['coef'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "temp特征是最重要的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.1 模型评价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LinearRegression on test is 0.291160231472\n",
      "The r2 score of LinearRegression on train is 0.728342118311\n"
     ]
    }
   ],
   "source": [
    "# 使用r2_score评价模型在测试集和训练集上的性能，并输出评估结果\n",
    "#测试集\n",
    "print ('The r2 score of LinearRegression on test is',r2_score(y_test, y_test_pred_lr))\n",
    "#训练集\n",
    "print ('The r2 score of LinearRegression on train is', r2_score(y_train, y_train_pred_lr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x112dc7d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#在训练集上观察预测残差的分布，看是否符合模型假设：噪声为0均值的高斯噪声\n",
    "f, ax = plt.subplots(figsize=(7, 5)) \n",
    "f.tight_layout() \n",
    "ax.hist(y_train - y_train_pred_lr,bins=40, label='Residuals Linear', color='b', alpha=.5); \n",
    "ax.set_title(\"Histogram of Residuals\") \n",
    "ax.legend(loc='best');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "残差分布和高斯分布比较匹配，是一个正态分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wvfB3IwZOHaVVtUHenjgiZcuh+rMP2PnqQ6gq/s49OOiCSbTr2M3x81/7aJv1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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112f23240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#还可以观察预测值与真值的散点图\n",
    "plt.figure(figsize=(4, 3))\n",
    "plt.scatter(y_train, y_train_pred_lr)\n",
    "plt.plot([-3, 3], [-3, 3], '--k')   #数据已经标准化，3倍标准差即可\n",
    "plt.axis('tight')\n",
    "plt.xlabel('True price')\n",
    "plt.ylabel('Predicted price')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在y的真值大的部分预测效果不好"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wangchunming/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.22631141,  0.01669926, -0.20783757,  0.50209565,  0.17988867,\n",
       "       -0.0631653 , -0.13318036])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 线性模型，随机梯度下降优化模型参数\n",
    "# 随机梯度下降一般在大数据集上应用，其实本项目不适合用\n",
    "from sklearn.linear_model import SGDRegressor\n",
    "\n",
    "# 使用默认配置初始化线\n",
    "sgdr = SGDRegressor(max_iter=1000)\n",
    "\n",
    "# 训练：参数估计\n",
    "sgdr.fit(X_train, y_train)\n",
    "\n",
    "# 预测\n",
    "#sgdr_y_predict = sgdr.predict(X_test)\n",
    "\n",
    "sgdr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of default measurement of SGDRegressor on test is 0.291066328755\n",
      "The value of default measurement of SGDRegressor on train is 0.728117787531\n"
     ]
    }
   ],
   "source": [
    "# 使用SGDRegressor模型自带的评估模块(评价准则为r2_score)，并输出评估结果\n",
    "print ('The value of default measurement of SGDRegressor on test is', sgdr.score(X_test, y_test))\n",
    "print ('The value of default measurement of SGDRegressor on train is', sgdr.score(X_train, y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里由于样本数不多，SGDRegressor可能不如LinearRegression。 sklearn建议样本数超过10万采用SGDRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 正则化的线性回归（L2正则 --> 岭回归）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is 0.271141739392\n",
      "The r2 score of RidgeCV on train is 0.727595235083\n"
     ]
    }
   ],
   "source": [
    "#岭回归／L2正则\n",
    "#class sklearn.linear_model.RidgeCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, \n",
    "#                                  normalize=False, scoring=None, cv=None, gcv_mode=None, \n",
    "#                                  store_cv_values=False)\n",
    "from sklearn.linear_model import  RidgeCV\n",
    "\n",
    "#设置超参数（正则参数）范围\n",
    "alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "#n_alphas = 20\n",
    "#alphas = np.logspace(-5,2,n_alphas)\n",
    "\n",
    "#生成一个RidgeCV实例\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)  \n",
    "\n",
    "#模型训练\n",
    "ridge.fit(X_train, y_train)    \n",
    "\n",
    "#预测\n",
    "y_test_pred_ridge = ridge.predict(X_test)\n",
    "y_train_pred_ridge = ridge.predict(X_train)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print ('The r2 score of RidgeCV on test is', r2_score(y_test, y_test_pred_ridge))\n",
    "print ('The r2 score of RidgeCV on train is', r2_score(y_train, y_train_pred_ridge))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iY5RIREQismJrMQN7ZnHjpEHJDiVSSiQiIhH44NQ5Xiyq4I6ZQ8lM79i/ajv20YmIJMmz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\nc4F/i1l/L/DrCGO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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1adef438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 10.0\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[0.685898043475]</td>\n",
       "      <td>[0.371541310343]</td>\n",
       "      <td>temp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[0.227282363093]</td>\n",
       "      <td>[0.220928660831]</td>\n",
       "      <td>instant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[0.0164206873069]</td>\n",
       "      <td>[0.0156196716185]</td>\n",
       "      <td>workingday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[-0.00701909533463]</td>\n",
       "      <td>[0.301818985841]</td>\n",
       "      <td>atemp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[-0.0608277934045]</td>\n",
       "      <td>[-0.0623853683403]</td>\n",
       "      <td>hum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[-0.136472395537]</td>\n",
       "      <td>[-0.129283918533]</td>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[-0.209508309028]</td>\n",
       "      <td>[-0.202447154743]</td>\n",
       "      <td>weathersit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               coef_lr          coef_ridge     columns\n",
       "3     [0.685898043475]    [0.371541310343]        temp\n",
       "0     [0.227282363093]    [0.220928660831]     instant\n",
       "1    [0.0164206873069]   [0.0156196716185]  workingday\n",
       "4  [-0.00701909533463]    [0.301818985841]       atemp\n",
       "5   [-0.0608277934045]  [-0.0623853683403]         hum\n",
       "6    [-0.136472395537]   [-0.129283918533]   windspeed\n",
       "2    [-0.209508309028]   [-0.202447154743]  weathersit"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_mean = np.mean(ridge.cv_values_, axis = 0)\n",
    "plt.plot(np.log10(alphas), mse_mean.reshape(len(alphas),1)) \n",
    "\n",
    "#这是为了标出最佳参数的位置，不是必须\n",
    "#plt.plot(np.log10(ridge.alpha_)*np.ones(3), [0.28, 0.29, 0.30])\n",
    "\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "\n",
    "print ('alpha is:', ridge.alpha_)\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef_lr\":list((lr.coef_.T)), \"coef_ridge\":list((ridge.coef_.T))})\n",
    "fs.sort_values(by=['coef_lr'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 正则化的线性回归（L1正则 --> Lasso）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LassoCV on test is -0.347091667037\n",
      "The r2 score of LassoCV on train is 0.644220725885\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wangchunming/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/coordinate_descent.py:1094: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "#### Lasso／L1正则\n",
    "# class sklearn.linear_model.LassoCV(eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, \n",
    "#                                    normalize=False, precompute=’auto’, max_iter=1000, \n",
    "#                                    tol=0.0001, copy_X=True, cv=None, verbose=False, n_jobs=1,\n",
    "#                                    positive=False, random_state=None, selection=’cyclic’)\n",
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "#设置超参数搜索范围\n",
    "#alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "\n",
    "#生成一个LassoCV实例\n",
    "#lasso = LassoCV(alphas=alphas)  \n",
    "lasso = LassoCV()  \n",
    "\n",
    "#训练（内含CV）\n",
    "lasso.fit(X_train, y_train)  \n",
    "\n",
    "#测试\n",
    "y_test_pred_lasso = lasso.predict(X_test)\n",
    "y_train_pred_lasso = lasso.predict(X_train)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print ('The r2 score of LassoCV on test is', r2_score(y_test, y_test_pred_lasso))\n",
    "print ('The r2 score of LassoCV on train is', r2_score(y_train, y_train_pred_lasso))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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m/mJmmUAi8BPn3AEA51yNmd1N4Pq5e7qzoODprv9WKEh30RGDiIg0\no85nERFpRsEgIiLNKBhERKQZBYOIiDSjYBARkWYUDCIi0sz/B2ZrWM11UiyNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1ae1e710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 0.167031966389\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef_lasso</th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[0.685898043475]</td>\n",
       "      <td>[0.371541310343]</td>\n",
       "      <td>temp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.101938</td>\n",
       "      <td>[0.227282363093]</td>\n",
       "      <td>[0.220928660831]</td>\n",
       "      <td>instant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[0.0164206873069]</td>\n",
       "      <td>[0.0156196716185]</td>\n",
       "      <td>workingday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.568091</td>\n",
       "      <td>[-0.00701909533463]</td>\n",
       "      <td>[0.301818985841]</td>\n",
       "      <td>atemp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>[-0.0608277934045]</td>\n",
       "      <td>[-0.0623853683403]</td>\n",
       "      <td>hum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>[-0.136472395537]</td>\n",
       "      <td>[-0.129283918533]</td>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.096913</td>\n",
       "      <td>[-0.209508309028]</td>\n",
       "      <td>[-0.202447154743]</td>\n",
       "      <td>weathersit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   coef_lasso              coef_lr          coef_ridge     columns\n",
       "3    0.000000     [0.685898043475]    [0.371541310343]        temp\n",
       "0    0.101938     [0.227282363093]    [0.220928660831]     instant\n",
       "1    0.000000    [0.0164206873069]   [0.0156196716185]  workingday\n",
       "4    0.568091  [-0.00701909533463]    [0.301818985841]       atemp\n",
       "5   -0.000000   [-0.0608277934045]  [-0.0623853683403]         hum\n",
       "6   -0.000000    [-0.136472395537]   [-0.129283918533]   windspeed\n",
       "2   -0.096913    [-0.209508309028]   [-0.202447154743]  weathersit"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mses = np.mean(lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log10(lasso.alphas_), mses) \n",
    "#plt.plot(np.log10(lasso.alphas_)*np.ones(3), [0.3, 0.4, 1.0])\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()    \n",
    "            \n",
    "print ('alpha is:', lasso.alpha_)\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef_lr\":list((lr.coef_.T)), \"coef_ridge\":list((ridge.coef_.T)), \"coef_lasso\":list((lasso.coef_.T))})\n",
    "fs.sort_values(by=['coef_lr'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RpGDQ6mgSkTJTE7ntnFEsv+tc7r/hNNJTElj03Hrm3vMGD7y5jYpjdV6XKDFm5yGtqtpI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112fa4978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 0.167031966389\n"
     ]
    }
   ],
   "source": [
    "mses = np.mean(lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log10(lasso.alphas_), mses) \n",
    "#plt.plot(np.log10(lasso.alphas_)*np.ones(3), [0.3, 0.4, 1.0])\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()    \n",
    "            \n",
    "print ('alpha is:', lasso.alpha_)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
